https://www.kaggle.com/code/prasundatta/aptosblindness1/notebook
Imagine being able to detect blindness before it happened.
Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working aged adults. Aravind Eye Hospital in India hopes to detect and prevent this disease among people living in rural areas where medical screening is difficult to conduct. Successful entries in this competition will improve the hospital’s ability to identify potential patients. Further, the solutions will be spread to other Ophthalmologists through the 4th Asia Pacific Tele-Ophthalmology Society (APTOS) Symposium
Currently, Aravind technicians travel to these rural areas to capture images and then rely on highly trained doctors to review the images and provide diagnosis. Their goal is to scale their efforts through technology; to gain the ability to automatically screen images for disease and provide information on how severe the condition may be.
In this synchronous Kernels-only competition, you'll build a machine learning model to speed up disease detection. You’ll work with thousands of images collected in rural areas to help identify diabetic retinopathy automatically. If successful, you will not only help to prevent lifelong blindness, but these models may be used to detect other sorts of diseases in the future, like glaucoma and macular degeneration.
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You are provided with a large set of retina images taken using fundus photography under a variety of imaging conditions.
A clinician has rated each image for the severity of diabetic retinopathy on a scale of 0 to 4:
0 - No DR
1 - Mild
2 - Moderate
3 - Severe
4 - Proliferative DR
Like any real-world data set, you will encounter noise in both the images and labels. Images may contain artifacts, be out of focus, underexposed, or overexposed. The images were gathered from multiple clinics using a variety of cameras over an extended period of time, which will introduce further variation.
You are predicting a binary target for each image. Your model should predict the probability (floating point) between 0.0 and 1.0 that the lesion in the image is malignant (the target). In the training data, train.csv, the value 0 denotes benign, and 1 indicates malignant.
- train.csv => the training set
- test.csv => the test set
- train.zip => the training set images.
- test.zip => the public test set imags.